Run spike sorting on concatenated recordings

In several experiments, several recordings are performed in sequence, for example a baseline/intervention. In these cases, since the underlying spiking activity can be assumed to be the same (or at least very similar), the recordings can be concatenated. This notebook shows how to concatenate the recordings before spike sorting and how to split the sorted output based on the concatenation.

Note that some sorters (tridesclous, …) handle directly multi segment paradigm, in that case we will use the append_recordings() function. Many sorters do not handle multi segment, in that case we will use the concatenate_recordings() function.

See also “Append and/or concatenate segments” in core tutorials.

import spikeinterface.full as si

When performing an experiment with multiple consecutive recordings, it can be a good idea to concatenate the single recordings, as this can improve the spike sorting performance and it doesn’t require to track the neurons over the different recordings.

Let’s create a toy example with 4 channels (the dumpable=True dumps the extractors to a file, which is required for parallel sorting):

recording_single, _ = si.toy_example(duration=10, num_channels=4, seed=0, num_segments=1)
print(recording_single)

# make dumpable
recording_single = recording_single.save()

NumpyRecording: 4 channels - 1 segments - 30.0kHz - 10.000s
Use cache_folder=/tmp/spikeinterface_cache/tmp56g1o6jm/XE0ELO3H
write_binary_recording with n_jobs = 1 and chunk_size = None

Let’s now assume that we have 4 recordings. In our case we will concatenate the recording_single 4 times. We first need to build a list of RecordingExtractor objects:

recordings_list = []
for i in range(4):
    recordings_list.append(recording_single)

Case 1. : the sorter handle multi segment

multirecording = si.append_recordings(recordings_list)
# lets put a probe
multirecording = multirecording.set_probe(recording_single.get_probe())
print(multirecording)

# run tridesclous in multi segment mode
multisorting = si.run_tridesclous(multirecording)
print(multisorting)

AppendSegmentRecording: 4 channels - 4 segments - 30.0kHz - 40.000s
/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.95.0/lib/python3.8/site-packages/tridesclous/dataio.py:175: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  v1 = distutils.version.LooseVersion(tridesclous_version).version
/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.95.0/lib/python3.8/site-packages/tridesclous/dataio.py:176: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  v2 = distutils.version.LooseVersion(self.info['tridesclous_version']).version
/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.95.0/lib/python3.8/site-packages/tridesclous/dataio.py:175: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  v1 = distutils.version.LooseVersion(tridesclous_version).version
/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.95.0/lib/python3.8/site-packages/tridesclous/dataio.py:176: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  v2 = distutils.version.LooseVersion(self.info['tridesclous_version']).version
TridesclousSortingExtractor: 9 units - 4 segments - 30.0kHz

Case 2. : the sorter DO NOT handle multi segment In that case the concatenate_recordings() mimic a mono segment that concatenate all segment

multirecording = si.concatenate_recordings(recordings_list)
# lets put a probe
multirecording = multirecording.set_probe(recording_single.get_probe())
print(multirecording)

# run klusta in mono segment mode
# multisorting = si.run_klusta(multirecording)

ConcatenateSegmentRecording: 4 channels - 1 segments - 30.0kHz - 40.000s